CfnApplicationV2¶
-
class
aws_cdk.aws_kinesisanalytics.
CfnApplicationV2
(scope, id, *, runtime_environment, service_execution_role, application_configuration=None, application_description=None, application_mode=None, application_name=None, tags=None)¶ Bases:
aws_cdk.core.CfnResource
A CloudFormation
AWS::KinesisAnalyticsV2::Application
.Creates an Amazon Kinesis Data Analytics application. For information about creating a Kinesis Data Analytics application, see Creating an Application .
- CloudformationResource
AWS::KinesisAnalyticsV2::Application
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics # property_map: Any cfn_application_v2 = kinesisanalytics.CfnApplicationV2(self, "MyCfnApplicationV2", runtime_environment="runtimeEnvironment", service_execution_role="serviceExecutionRole", # the properties below are optional application_configuration=kinesisanalytics.CfnApplicationV2.ApplicationConfigurationProperty( application_code_configuration=kinesisanalytics.CfnApplicationV2.ApplicationCodeConfigurationProperty( code_content=kinesisanalytics.CfnApplicationV2.CodeContentProperty( s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentLocationProperty( bucket_arn="bucketArn", file_key="fileKey", object_version="objectVersion" ), text_content="textContent", zip_file_content="zipFileContent" ), code_content_type="codeContentType" ), application_snapshot_configuration=kinesisanalytics.CfnApplicationV2.ApplicationSnapshotConfigurationProperty( snapshots_enabled=False ), environment_properties=kinesisanalytics.CfnApplicationV2.EnvironmentPropertiesProperty( property_groups=[kinesisanalytics.CfnApplicationV2.PropertyGroupProperty( property_group_id="propertyGroupId", property_map=property_map )] ), flink_application_configuration=kinesisanalytics.CfnApplicationV2.FlinkApplicationConfigurationProperty( checkpoint_configuration=kinesisanalytics.CfnApplicationV2.CheckpointConfigurationProperty( configuration_type="configurationType", # the properties below are optional checkpointing_enabled=False, checkpoint_interval=123, min_pause_between_checkpoints=123 ), monitoring_configuration=kinesisanalytics.CfnApplicationV2.MonitoringConfigurationProperty( configuration_type="configurationType", # the properties below are optional log_level="logLevel", metrics_level="metricsLevel" ), parallelism_configuration=kinesisanalytics.CfnApplicationV2.ParallelismConfigurationProperty( configuration_type="configurationType", # the properties below are optional auto_scaling_enabled=False, parallelism=123, parallelism_per_kpu=123 ) ), sql_application_configuration=kinesisanalytics.CfnApplicationV2.SqlApplicationConfigurationProperty( inputs=[kinesisanalytics.CfnApplicationV2.InputProperty( input_schema=kinesisanalytics.CfnApplicationV2.InputSchemaProperty( record_columns=[kinesisanalytics.CfnApplicationV2.RecordColumnProperty( name="name", sql_type="sqlType", # the properties below are optional mapping="mapping" )], record_format=kinesisanalytics.CfnApplicationV2.RecordFormatProperty( record_format_type="recordFormatType", # the properties below are optional mapping_parameters=kinesisanalytics.CfnApplicationV2.MappingParametersProperty( csv_mapping_parameters=kinesisanalytics.CfnApplicationV2.CSVMappingParametersProperty( record_column_delimiter="recordColumnDelimiter", record_row_delimiter="recordRowDelimiter" ), json_mapping_parameters=kinesisanalytics.CfnApplicationV2.JSONMappingParametersProperty( record_row_path="recordRowPath" ) ) ), # the properties below are optional record_encoding="recordEncoding" ), name_prefix="namePrefix", # the properties below are optional input_parallelism=kinesisanalytics.CfnApplicationV2.InputParallelismProperty( count=123 ), input_processing_configuration=kinesisanalytics.CfnApplicationV2.InputProcessingConfigurationProperty( input_lambda_processor=kinesisanalytics.CfnApplicationV2.InputLambdaProcessorProperty( resource_arn="resourceArn" ) ), kinesis_firehose_input=kinesisanalytics.CfnApplicationV2.KinesisFirehoseInputProperty( resource_arn="resourceArn" ), kinesis_streams_input=kinesisanalytics.CfnApplicationV2.KinesisStreamsInputProperty( resource_arn="resourceArn" ) )] ), zeppelin_application_configuration=kinesisanalytics.CfnApplicationV2.ZeppelinApplicationConfigurationProperty( catalog_configuration=kinesisanalytics.CfnApplicationV2.CatalogConfigurationProperty( glue_data_catalog_configuration=kinesisanalytics.CfnApplicationV2.GlueDataCatalogConfigurationProperty( database_arn="databaseArn" ) ), custom_artifacts_configuration=[kinesisanalytics.CfnApplicationV2.CustomArtifactConfigurationProperty( artifact_type="artifactType", # the properties below are optional maven_reference=kinesisanalytics.CfnApplicationV2.MavenReferenceProperty( artifact_id="artifactId", group_id="groupId", version="version" ), s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentLocationProperty( bucket_arn="bucketArn", file_key="fileKey", object_version="objectVersion" ) )], deploy_as_application_configuration=kinesisanalytics.CfnApplicationV2.DeployAsApplicationConfigurationProperty( s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentBaseLocationProperty( base_path="basePath", bucket_arn="bucketArn" ) ), monitoring_configuration=kinesisanalytics.CfnApplicationV2.ZeppelinMonitoringConfigurationProperty( log_level="logLevel" ) ) ), application_description="applicationDescription", application_mode="applicationMode", application_name="applicationName", tags=[CfnTag( key="key", value="value" )] )
Create a new
AWS::KinesisAnalyticsV2::Application
.- Parameters
scope (
Construct
) –scope in which this resource is defined.
id (
str
) –scoped id of the resource.
runtime_environment (
str
) – The runtime environment for the application.service_execution_role (
str
) – Specifies the IAM role that the application uses to access external resources.application_configuration (
Union
[IResolvable
,ApplicationConfigurationProperty
,None
]) – Use this parameter to configure the application.application_description (
Optional
[str
]) – The description of the application.application_mode (
Optional
[str
]) – To create a Kinesis Data Analytics Studio notebook, you must set the mode toINTERACTIVE
. However, for a Kinesis Data Analytics for Apache Flink application, the mode is optional.application_name (
Optional
[str
]) – The name of the application.tags (
Optional
[Sequence
[CfnTag
]]) – A list of one or more tags to assign to the application. A tag is a key-value pair that identifies an application. Note that the maximum number of application tags includes system tags. The maximum number of user-defined application tags is 50.
Methods
-
add_deletion_override
(path)¶ Syntactic sugar for
addOverride(path, undefined)
.- Parameters
path (
str
) – The path of the value to delete.- Return type
None
-
add_depends_on
(target)¶ Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.
This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.
- Parameters
target (
CfnResource
) –- Return type
None
-
add_metadata
(key, value)¶ Add a value to the CloudFormation Resource Metadata.
- Parameters
key (
str
) –value (
Any
) –
- See
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html
Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.
- Return type
None
-
add_override
(path, value)¶ Adds an override to the synthesized CloudFormation resource.
To add a property override, either use
addPropertyOverride
or prefixpath
with “Properties.” (i.e.Properties.TopicName
).If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.
To include a literal
.
in the property name, prefix with a\
. In most programming languages you will need to write this as"\\."
because the\
itself will need to be escaped.For example:
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"]) cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")
would add the overrides Example:
"Properties": { "GlobalSecondaryIndexes": [ { "Projection": { "NonKeyAttributes": [ "myattribute" ] ... } ... }, { "ProjectionType": "INCLUDE" ... }, ] ... }
The
value
argument toaddOverride
will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.- Parameters
path (
str
) –The path of the property, you can use dot notation to override values in complex types. Any intermdediate keys will be created as needed.
value (
Any
) –The value. Could be primitive or complex.
- Return type
None
-
add_property_deletion_override
(property_path)¶ Adds an override that deletes the value of a property from the resource definition.
- Parameters
property_path (
str
) – The path to the property.- Return type
None
-
add_property_override
(property_path, value)¶ Adds an override to a resource property.
Syntactic sugar for
addOverride("Properties.<...>", value)
.- Parameters
property_path (
str
) – The path of the property.value (
Any
) – The value.
- Return type
None
-
apply_removal_policy
(policy=None, *, apply_to_update_replace_policy=None, default=None)¶ Sets the deletion policy of the resource based on the removal policy specified.
The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.
The resource can be deleted (
RemovalPolicy.DESTROY
), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN
).- Parameters
policy (
Optional
[RemovalPolicy
]) –apply_to_update_replace_policy (
Optional
[bool
]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: truedefault (
Optional
[RemovalPolicy
]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resoure, please consult that specific resource’s documentation.
- Return type
None
-
get_att
(attribute_name)¶ Returns a token for an runtime attribute of this resource.
Ideally, use generated attribute accessors (e.g.
resource.arn
), but this can be used for future compatibility in case there is no generated attribute.- Parameters
attribute_name (
str
) – The name of the attribute.- Return type
-
get_metadata
(key)¶ Retrieve a value value from the CloudFormation Resource Metadata.
- Parameters
key (
str
) –- See
https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html
Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.
- Return type
Any
-
inspect
(inspector)¶ Examines the CloudFormation resource and discloses attributes.
- Parameters
inspector (
TreeInspector
) –tree inspector to collect and process attributes.
- Return type
None
-
override_logical_id
(new_logical_id)¶ Overrides the auto-generated logical ID with a specific ID.
- Parameters
new_logical_id (
str
) – The new logical ID to use for this stack element.- Return type
None
-
to_string
()¶ Returns a string representation of this construct.
- Return type
str
- Returns
a string representation of this resource
Attributes
-
CFN_RESOURCE_TYPE_NAME
= 'AWS::KinesisAnalyticsV2::Application'¶
-
application_configuration
¶ Use this parameter to configure the application.
-
application_description
¶ The description of the application.
-
application_mode
¶ To create a Kinesis Data Analytics Studio notebook, you must set the mode to
INTERACTIVE
.However, for a Kinesis Data Analytics for Apache Flink application, the mode is optional.
-
application_name
¶ The name of the application.
-
cfn_options
¶ Options for this resource, such as condition, update policy etc.
- Return type
-
cfn_resource_type
¶ AWS resource type.
- Return type
str
-
creation_stack
¶ return:
the stack trace of the point where this Resource was created from, sourced from the +metadata+ entry typed +aws:cdk:logicalId+, and with the bottom-most node +internal+ entries filtered.
- Return type
List
[str
]
-
logical_id
¶ The logical ID for this CloudFormation stack element.
The logical ID of the element is calculated from the path of the resource node in the construct tree.
To override this value, use
overrideLogicalId(newLogicalId)
.- Return type
str
- Returns
the logical ID as a stringified token. This value will only get resolved during synthesis.
-
node
¶ The construct tree node associated with this construct.
- Return type
-
ref
¶ Return a string that will be resolved to a CloudFormation
{ Ref }
for this element.If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through
Lazy.any({ produce: resource.ref })
.- Return type
str
-
runtime_environment
¶ The runtime environment for the application.
-
service_execution_role
¶ Specifies the IAM role that the application uses to access external resources.
-
stack
¶ The stack in which this element is defined.
CfnElements must be defined within a stack scope (directly or indirectly).
- Return type
A list of one or more tags to assign to the application.
A tag is a key-value pair that identifies an application. Note that the maximum number of application tags includes system tags. The maximum number of user-defined application tags is 50.
Static Methods
-
classmethod
is_cfn_element
(x)¶ Returns
true
if a construct is a stack element (i.e. part of the synthesized cloudformation template).Uses duck-typing instead of
instanceof
to allow stack elements from different versions of this library to be included in the same stack.- Parameters
x (
Any
) –- Return type
bool
- Returns
The construct as a stack element or undefined if it is not a stack element.
-
classmethod
is_cfn_resource
(construct)¶ Check whether the given construct is a CfnResource.
- Parameters
construct (
IConstruct
) –- Return type
bool
-
classmethod
is_construct
(x)¶ Return whether the given object is a Construct.
- Parameters
x (
Any
) –- Return type
bool
ApplicationCodeConfigurationProperty¶
-
class
CfnApplicationV2.
ApplicationCodeConfigurationProperty
(*, code_content, code_content_type)¶ Bases:
object
Describes code configuration for an application.
- Parameters
code_content (
Union
[IResolvable
,CodeContentProperty
]) – The location and type of the application code.code_content_type (
str
) – Specifies whether the code content is in text or zip format.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics application_code_configuration_property = kinesisanalytics.CfnApplicationV2.ApplicationCodeConfigurationProperty( code_content=kinesisanalytics.CfnApplicationV2.CodeContentProperty( s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentLocationProperty( bucket_arn="bucketArn", file_key="fileKey", object_version="objectVersion" ), text_content="textContent", zip_file_content="zipFileContent" ), code_content_type="codeContentType" )
Attributes
-
code_content
¶ The location and type of the application code.
-
code_content_type
¶ Specifies whether the code content is in text or zip format.
ApplicationConfigurationProperty¶
-
class
CfnApplicationV2.
ApplicationConfigurationProperty
(*, application_code_configuration=None, application_snapshot_configuration=None, environment_properties=None, flink_application_configuration=None, sql_application_configuration=None, zeppelin_application_configuration=None)¶ Bases:
object
Specifies the creation parameters for a Kinesis Data Analytics application.
- Parameters
application_code_configuration (
Union
[IResolvable
,ApplicationCodeConfigurationProperty
,None
]) – The code location and type parameters for a Flink-based Kinesis Data Analytics application.application_snapshot_configuration (
Union
[IResolvable
,ApplicationSnapshotConfigurationProperty
,None
]) – Describes whether snapshots are enabled for a Flink-based Kinesis Data Analytics application.environment_properties (
Union
[IResolvable
,EnvironmentPropertiesProperty
,None
]) – Describes execution properties for a Flink-based Kinesis Data Analytics application.flink_application_configuration (
Union
[IResolvable
,FlinkApplicationConfigurationProperty
,None
]) – The creation and update parameters for a Flink-based Kinesis Data Analytics application.sql_application_configuration (
Union
[IResolvable
,SqlApplicationConfigurationProperty
,None
]) – The creation and update parameters for a SQL-based Kinesis Data Analytics application.zeppelin_application_configuration (
Union
[IResolvable
,ZeppelinApplicationConfigurationProperty
,None
]) – The configuration parameters for a Kinesis Data Analytics Studio notebook.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics # property_map: Any application_configuration_property = kinesisanalytics.CfnApplicationV2.ApplicationConfigurationProperty( application_code_configuration=kinesisanalytics.CfnApplicationV2.ApplicationCodeConfigurationProperty( code_content=kinesisanalytics.CfnApplicationV2.CodeContentProperty( s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentLocationProperty( bucket_arn="bucketArn", file_key="fileKey", object_version="objectVersion" ), text_content="textContent", zip_file_content="zipFileContent" ), code_content_type="codeContentType" ), application_snapshot_configuration=kinesisanalytics.CfnApplicationV2.ApplicationSnapshotConfigurationProperty( snapshots_enabled=False ), environment_properties=kinesisanalytics.CfnApplicationV2.EnvironmentPropertiesProperty( property_groups=[kinesisanalytics.CfnApplicationV2.PropertyGroupProperty( property_group_id="propertyGroupId", property_map=property_map )] ), flink_application_configuration=kinesisanalytics.CfnApplicationV2.FlinkApplicationConfigurationProperty( checkpoint_configuration=kinesisanalytics.CfnApplicationV2.CheckpointConfigurationProperty( configuration_type="configurationType", # the properties below are optional checkpointing_enabled=False, checkpoint_interval=123, min_pause_between_checkpoints=123 ), monitoring_configuration=kinesisanalytics.CfnApplicationV2.MonitoringConfigurationProperty( configuration_type="configurationType", # the properties below are optional log_level="logLevel", metrics_level="metricsLevel" ), parallelism_configuration=kinesisanalytics.CfnApplicationV2.ParallelismConfigurationProperty( configuration_type="configurationType", # the properties below are optional auto_scaling_enabled=False, parallelism=123, parallelism_per_kpu=123 ) ), sql_application_configuration=kinesisanalytics.CfnApplicationV2.SqlApplicationConfigurationProperty( inputs=[kinesisanalytics.CfnApplicationV2.InputProperty( input_schema=kinesisanalytics.CfnApplicationV2.InputSchemaProperty( record_columns=[kinesisanalytics.CfnApplicationV2.RecordColumnProperty( name="name", sql_type="sqlType", # the properties below are optional mapping="mapping" )], record_format=kinesisanalytics.CfnApplicationV2.RecordFormatProperty( record_format_type="recordFormatType", # the properties below are optional mapping_parameters=kinesisanalytics.CfnApplicationV2.MappingParametersProperty( csv_mapping_parameters=kinesisanalytics.CfnApplicationV2.CSVMappingParametersProperty( record_column_delimiter="recordColumnDelimiter", record_row_delimiter="recordRowDelimiter" ), json_mapping_parameters=kinesisanalytics.CfnApplicationV2.JSONMappingParametersProperty( record_row_path="recordRowPath" ) ) ), # the properties below are optional record_encoding="recordEncoding" ), name_prefix="namePrefix", # the properties below are optional input_parallelism=kinesisanalytics.CfnApplicationV2.InputParallelismProperty( count=123 ), input_processing_configuration=kinesisanalytics.CfnApplicationV2.InputProcessingConfigurationProperty( input_lambda_processor=kinesisanalytics.CfnApplicationV2.InputLambdaProcessorProperty( resource_arn="resourceArn" ) ), kinesis_firehose_input=kinesisanalytics.CfnApplicationV2.KinesisFirehoseInputProperty( resource_arn="resourceArn" ), kinesis_streams_input=kinesisanalytics.CfnApplicationV2.KinesisStreamsInputProperty( resource_arn="resourceArn" ) )] ), zeppelin_application_configuration=kinesisanalytics.CfnApplicationV2.ZeppelinApplicationConfigurationProperty( catalog_configuration=kinesisanalytics.CfnApplicationV2.CatalogConfigurationProperty( glue_data_catalog_configuration=kinesisanalytics.CfnApplicationV2.GlueDataCatalogConfigurationProperty( database_arn="databaseArn" ) ), custom_artifacts_configuration=[kinesisanalytics.CfnApplicationV2.CustomArtifactConfigurationProperty( artifact_type="artifactType", # the properties below are optional maven_reference=kinesisanalytics.CfnApplicationV2.MavenReferenceProperty( artifact_id="artifactId", group_id="groupId", version="version" ), s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentLocationProperty( bucket_arn="bucketArn", file_key="fileKey", object_version="objectVersion" ) )], deploy_as_application_configuration=kinesisanalytics.CfnApplicationV2.DeployAsApplicationConfigurationProperty( s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentBaseLocationProperty( base_path="basePath", bucket_arn="bucketArn" ) ), monitoring_configuration=kinesisanalytics.CfnApplicationV2.ZeppelinMonitoringConfigurationProperty( log_level="logLevel" ) ) )
Attributes
-
application_code_configuration
¶ The code location and type parameters for a Flink-based Kinesis Data Analytics application.
- Link
- Return type
Union
[IResolvable
,ApplicationCodeConfigurationProperty
,None
]
-
application_snapshot_configuration
¶ Describes whether snapshots are enabled for a Flink-based Kinesis Data Analytics application.
- Link
- Return type
Union
[IResolvable
,ApplicationSnapshotConfigurationProperty
,None
]
-
environment_properties
¶ Describes execution properties for a Flink-based Kinesis Data Analytics application.
-
flink_application_configuration
¶ The creation and update parameters for a Flink-based Kinesis Data Analytics application.
- Link
- Return type
Union
[IResolvable
,FlinkApplicationConfigurationProperty
,None
]
-
sql_application_configuration
¶ The creation and update parameters for a SQL-based Kinesis Data Analytics application.
- Link
- Return type
Union
[IResolvable
,SqlApplicationConfigurationProperty
,None
]
-
zeppelin_application_configuration
¶ The configuration parameters for a Kinesis Data Analytics Studio notebook.
- Link
- Return type
Union
[IResolvable
,ZeppelinApplicationConfigurationProperty
,None
]
ApplicationSnapshotConfigurationProperty¶
-
class
CfnApplicationV2.
ApplicationSnapshotConfigurationProperty
(*, snapshots_enabled)¶ Bases:
object
Describes whether snapshots are enabled for a Flink-based Kinesis Data Analytics application.
- Parameters
snapshots_enabled (
Union
[bool
,IResolvable
]) – Describes whether snapshots are enabled for a Flink-based Kinesis Data Analytics application.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics application_snapshot_configuration_property = kinesisanalytics.CfnApplicationV2.ApplicationSnapshotConfigurationProperty( snapshots_enabled=False )
Attributes
-
snapshots_enabled
¶ Describes whether snapshots are enabled for a Flink-based Kinesis Data Analytics application.
CSVMappingParametersProperty¶
-
class
CfnApplicationV2.
CSVMappingParametersProperty
(*, record_column_delimiter, record_row_delimiter)¶ Bases:
object
For a SQL-based Kinesis Data Analytics application, provides additional mapping information when the record format uses delimiters, such as CSV.
For example, the following sample records use CSV format, where the records use the ‘n’ as the row delimiter and a comma (“,”) as the column delimiter:
"name1", "address1"
"name2", "address2"
- Parameters
record_column_delimiter (
str
) – The column delimiter. For example, in a CSV format, a comma (“,”) is the typical column delimiter.record_row_delimiter (
str
) – The row delimiter. For example, in a CSV format, ‘n’ is the typical row delimiter.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics c_sVMapping_parameters_property = kinesisanalytics.CfnApplicationV2.CSVMappingParametersProperty( record_column_delimiter="recordColumnDelimiter", record_row_delimiter="recordRowDelimiter" )
Attributes
-
record_column_delimiter
¶ The column delimiter.
For example, in a CSV format, a comma (“,”) is the typical column delimiter.
-
record_row_delimiter
¶ The row delimiter.
For example, in a CSV format, ‘n’ is the typical row delimiter.
CatalogConfigurationProperty¶
-
class
CfnApplicationV2.
CatalogConfigurationProperty
(*, glue_data_catalog_configuration=None)¶ Bases:
object
The configuration parameters for the default Amazon Glue database.
You use this database for SQL queries that you write in a Kinesis Data Analytics Studio notebook.
- Parameters
glue_data_catalog_configuration (
Union
[IResolvable
,GlueDataCatalogConfigurationProperty
,None
]) – The configuration parameters for the default Amazon Glue database. You use this database for Apache Flink SQL queries and table API transforms that you write in a Kinesis Data Analytics Studio notebook.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics catalog_configuration_property = kinesisanalytics.CfnApplicationV2.CatalogConfigurationProperty( glue_data_catalog_configuration=kinesisanalytics.CfnApplicationV2.GlueDataCatalogConfigurationProperty( database_arn="databaseArn" ) )
Attributes
-
glue_data_catalog_configuration
¶ The configuration parameters for the default Amazon Glue database.
You use this database for Apache Flink SQL queries and table API transforms that you write in a Kinesis Data Analytics Studio notebook.
CheckpointConfigurationProperty¶
-
class
CfnApplicationV2.
CheckpointConfigurationProperty
(*, configuration_type, checkpointing_enabled=None, checkpoint_interval=None, min_pause_between_checkpoints=None)¶ Bases:
object
Describes an application’s checkpointing configuration.
Checkpointing is the process of persisting application state for fault tolerance. For more information, see Checkpoints for Fault Tolerance in the Apache Flink Documentation .
- Parameters
configuration_type (
str
) – Describes whether the application uses Kinesis Data Analytics’ default checkpointing behavior. You must set this property toCUSTOM
in order to set theCheckpointingEnabled
,CheckpointInterval
, orMinPauseBetweenCheckpoints
parameters. .. epigraph:: If this value is set toDEFAULT
, the application will use the following values, even if they are set to other values using APIs or application code: - CheckpointingEnabled: true - CheckpointInterval: 60000 - MinPauseBetweenCheckpoints: 5000checkpointing_enabled (
Union
[bool
,IResolvable
,None
]) – Describes whether checkpointing is enabled for a Flink-based Kinesis Data Analytics application. .. epigraph:: IfCheckpointConfiguration.ConfigurationType
isDEFAULT
, the application will use aCheckpointingEnabled
value oftrue
, even if this value is set to another value using this API or in application code.checkpoint_interval (
Union
[int
,float
,None
]) – Describes the interval in milliseconds between checkpoint operations. .. epigraph:: IfCheckpointConfiguration.ConfigurationType
isDEFAULT
, the application will use aCheckpointInterval
value of 60000, even if this value is set to another value using this API or in application code.min_pause_between_checkpoints (
Union
[int
,float
,None
]) –Describes the minimum time in milliseconds after a checkpoint operation completes that a new checkpoint operation can start. If a checkpoint operation takes longer than the
CheckpointInterval
, the application otherwise performs continual checkpoint operations. For more information, see Tuning Checkpointing in the Apache Flink Documentation . .. epigraph:: IfCheckpointConfiguration.ConfigurationType
isDEFAULT
, the application will use aMinPauseBetweenCheckpoints
value of 5000, even if this value is set using this API or in application code.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics checkpoint_configuration_property = kinesisanalytics.CfnApplicationV2.CheckpointConfigurationProperty( configuration_type="configurationType", # the properties below are optional checkpointing_enabled=False, checkpoint_interval=123, min_pause_between_checkpoints=123 )
Attributes
-
checkpoint_interval
¶ Describes the interval in milliseconds between checkpoint operations.
If
CheckpointConfiguration.ConfigurationType
isDEFAULT
, the application will use aCheckpointInterval
value of 60000, even if this value is set to another value using this API or in application code.
-
checkpointing_enabled
¶ Describes whether checkpointing is enabled for a Flink-based Kinesis Data Analytics application.
If
CheckpointConfiguration.ConfigurationType
isDEFAULT
, the application will use aCheckpointingEnabled
value oftrue
, even if this value is set to another value using this API or in application code.
-
configuration_type
¶ Describes whether the application uses Kinesis Data Analytics’ default checkpointing behavior.
You must set this property to
CUSTOM
in order to set theCheckpointingEnabled
,CheckpointInterval
, orMinPauseBetweenCheckpoints
parameters. .. epigraph:If this value is set to ``DEFAULT`` , the application will use the following values, even if they are set to other values using APIs or application code: - *CheckpointingEnabled:* true - *CheckpointInterval:* 60000 - *MinPauseBetweenCheckpoints:* 5000
-
min_pause_between_checkpoints
¶ Describes the minimum time in milliseconds after a checkpoint operation completes that a new checkpoint operation can start.
If a checkpoint operation takes longer than the
CheckpointInterval
, the application otherwise performs continual checkpoint operations. For more information, see Tuning Checkpointing in the Apache Flink Documentation . .. epigraph:If ``CheckpointConfiguration.ConfigurationType`` is ``DEFAULT`` , the application will use a ``MinPauseBetweenCheckpoints`` value of 5000, even if this value is set using this API or in application code.
CodeContentProperty¶
-
class
CfnApplicationV2.
CodeContentProperty
(*, s3_content_location=None, text_content=None, zip_file_content=None)¶ Bases:
object
Specifies either the application code, or the location of the application code, for a Flink-based Kinesis Data Analytics application.
- Parameters
s3_content_location (
Union
[IResolvable
,S3ContentLocationProperty
,None
]) – Information about the Amazon S3 bucket that contains the application code.text_content (
Optional
[str
]) – The text-format code for a Flink-based Kinesis Data Analytics application.zip_file_content (
Optional
[str
]) – The zip-format code for a Flink-based Kinesis Data Analytics application.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics code_content_property = kinesisanalytics.CfnApplicationV2.CodeContentProperty( s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentLocationProperty( bucket_arn="bucketArn", file_key="fileKey", object_version="objectVersion" ), text_content="textContent", zip_file_content="zipFileContent" )
Attributes
-
s3_content_location
¶ Information about the Amazon S3 bucket that contains the application code.
-
text_content
¶ The text-format code for a Flink-based Kinesis Data Analytics application.
-
zip_file_content
¶ The zip-format code for a Flink-based Kinesis Data Analytics application.
CustomArtifactConfigurationProperty¶
-
class
CfnApplicationV2.
CustomArtifactConfigurationProperty
(*, artifact_type, maven_reference=None, s3_content_location=None)¶ Bases:
object
The configuration of connectors and user-defined functions.
- Parameters
artifact_type (
str
) – Set this to eitherUDF
orDEPENDENCY_JAR
.UDF
stands for user-defined functions. This type of artifact must be in an S3 bucket. ADEPENDENCY_JAR
can be in either Maven or an S3 bucket.maven_reference (
Union
[IResolvable
,MavenReferenceProperty
,None
]) – The parameters required to fully specify a Maven reference.s3_content_location (
Union
[IResolvable
,S3ContentLocationProperty
,None
]) – The location of the custom artifacts.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics custom_artifact_configuration_property = kinesisanalytics.CfnApplicationV2.CustomArtifactConfigurationProperty( artifact_type="artifactType", # the properties below are optional maven_reference=kinesisanalytics.CfnApplicationV2.MavenReferenceProperty( artifact_id="artifactId", group_id="groupId", version="version" ), s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentLocationProperty( bucket_arn="bucketArn", file_key="fileKey", object_version="objectVersion" ) )
Attributes
-
artifact_type
¶ Set this to either
UDF
orDEPENDENCY_JAR
.UDF
stands for user-defined functions. This type of artifact must be in an S3 bucket. ADEPENDENCY_JAR
can be in either Maven or an S3 bucket.
-
maven_reference
¶ The parameters required to fully specify a Maven reference.
-
s3_content_location
¶ The location of the custom artifacts.
DeployAsApplicationConfigurationProperty¶
-
class
CfnApplicationV2.
DeployAsApplicationConfigurationProperty
(*, s3_content_location)¶ Bases:
object
The information required to deploy a Kinesis Data Analytics Studio notebook as an application with durable state.
- Parameters
s3_content_location (
Union
[IResolvable
,S3ContentBaseLocationProperty
]) – The description of an Amazon S3 object that contains the Amazon Data Analytics application, including the Amazon Resource Name (ARN) of the S3 bucket, the name of the Amazon S3 object that contains the data, and the version number of the Amazon S3 object that contains the data.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics deploy_as_application_configuration_property = kinesisanalytics.CfnApplicationV2.DeployAsApplicationConfigurationProperty( s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentBaseLocationProperty( base_path="basePath", bucket_arn="bucketArn" ) )
Attributes
-
s3_content_location
¶ The description of an Amazon S3 object that contains the Amazon Data Analytics application, including the Amazon Resource Name (ARN) of the S3 bucket, the name of the Amazon S3 object that contains the data, and the version number of the Amazon S3 object that contains the data.
EnvironmentPropertiesProperty¶
-
class
CfnApplicationV2.
EnvironmentPropertiesProperty
(*, property_groups=None)¶ Bases:
object
Describes execution properties for a Flink-based Kinesis Data Analytics application.
- Parameters
property_groups (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,PropertyGroupProperty
]],None
]) – Describes the execution property groups.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics # property_map: Any environment_properties_property = kinesisanalytics.CfnApplicationV2.EnvironmentPropertiesProperty( property_groups=[kinesisanalytics.CfnApplicationV2.PropertyGroupProperty( property_group_id="propertyGroupId", property_map=property_map )] )
Attributes
-
property_groups
¶ Describes the execution property groups.
FlinkApplicationConfigurationProperty¶
-
class
CfnApplicationV2.
FlinkApplicationConfigurationProperty
(*, checkpoint_configuration=None, monitoring_configuration=None, parallelism_configuration=None)¶ Bases:
object
Describes configuration parameters for a Flink-based Kinesis Data Analytics application or a Studio notebook.
- Parameters
checkpoint_configuration (
Union
[IResolvable
,CheckpointConfigurationProperty
,None
]) –Describes an application’s checkpointing configuration. Checkpointing is the process of persisting application state for fault tolerance. For more information, see Checkpoints for Fault Tolerance in the Apache Flink Documentation .
monitoring_configuration (
Union
[IResolvable
,MonitoringConfigurationProperty
,None
]) – Describes configuration parameters for Amazon CloudWatch logging for an application.parallelism_configuration (
Union
[IResolvable
,ParallelismConfigurationProperty
,None
]) – Describes parameters for how an application executes multiple tasks simultaneously.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics flink_application_configuration_property = kinesisanalytics.CfnApplicationV2.FlinkApplicationConfigurationProperty( checkpoint_configuration=kinesisanalytics.CfnApplicationV2.CheckpointConfigurationProperty( configuration_type="configurationType", # the properties below are optional checkpointing_enabled=False, checkpoint_interval=123, min_pause_between_checkpoints=123 ), monitoring_configuration=kinesisanalytics.CfnApplicationV2.MonitoringConfigurationProperty( configuration_type="configurationType", # the properties below are optional log_level="logLevel", metrics_level="metricsLevel" ), parallelism_configuration=kinesisanalytics.CfnApplicationV2.ParallelismConfigurationProperty( configuration_type="configurationType", # the properties below are optional auto_scaling_enabled=False, parallelism=123, parallelism_per_kpu=123 ) )
Attributes
-
checkpoint_configuration
¶ Describes an application’s checkpointing configuration.
Checkpointing is the process of persisting application state for fault tolerance. For more information, see Checkpoints for Fault Tolerance in the Apache Flink Documentation .
- Link
- Return type
Union
[IResolvable
,CheckpointConfigurationProperty
,None
]
-
monitoring_configuration
¶ Describes configuration parameters for Amazon CloudWatch logging for an application.
- Link
- Return type
Union
[IResolvable
,MonitoringConfigurationProperty
,None
]
-
parallelism_configuration
¶ Describes parameters for how an application executes multiple tasks simultaneously.
- Link
- Return type
Union
[IResolvable
,ParallelismConfigurationProperty
,None
]
GlueDataCatalogConfigurationProperty¶
-
class
CfnApplicationV2.
GlueDataCatalogConfigurationProperty
(*, database_arn=None)¶ Bases:
object
The configuration of the Glue Data Catalog that you use for Apache Flink SQL queries and table API transforms that you write in an application.
- Parameters
database_arn (
Optional
[str
]) – The Amazon Resource Name (ARN) of the database.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics glue_data_catalog_configuration_property = kinesisanalytics.CfnApplicationV2.GlueDataCatalogConfigurationProperty( database_arn="databaseArn" )
Attributes
-
database_arn
¶ The Amazon Resource Name (ARN) of the database.
InputLambdaProcessorProperty¶
-
class
CfnApplicationV2.
InputLambdaProcessorProperty
(*, resource_arn)¶ Bases:
object
An object that contains the Amazon Resource Name (ARN) of the Amazon Lambda function that is used to preprocess records in the stream in a SQL-based Kinesis Data Analytics application.
- Parameters
resource_arn (
str
) – The ARN of the Amazon Lambda function that operates on records in the stream. .. epigraph:: To specify an earlier version of the Lambda function than the latest, include the Lambda function version in the Lambda function ARN. For more information about Lambda ARNs, see Example ARNs: Amazon Lambda- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics input_lambda_processor_property = kinesisanalytics.CfnApplicationV2.InputLambdaProcessorProperty( resource_arn="resourceArn" )
Attributes
-
resource_arn
¶ The ARN of the Amazon Lambda function that operates on records in the stream.
To specify an earlier version of the Lambda function than the latest, include the Lambda function version in the Lambda function ARN. For more information about Lambda ARNs, see Example ARNs: Amazon Lambda
InputParallelismProperty¶
-
class
CfnApplicationV2.
InputParallelismProperty
(*, count=None)¶ Bases:
object
For a SQL-based Kinesis Data Analytics application, describes the number of in-application streams to create for a given streaming source.
- Parameters
count (
Union
[int
,float
,None
]) – The number of in-application streams to create.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics input_parallelism_property = kinesisanalytics.CfnApplicationV2.InputParallelismProperty( count=123 )
Attributes
-
count
¶ The number of in-application streams to create.
InputProcessingConfigurationProperty¶
-
class
CfnApplicationV2.
InputProcessingConfigurationProperty
(*, input_lambda_processor=None)¶ Bases:
object
For an SQL-based Amazon Kinesis Data Analytics application, describes a processor that is used to preprocess the records in the stream before being processed by your application code.
Currently, the only input processor available is Amazon Lambda .
- Parameters
input_lambda_processor (
Union
[IResolvable
,InputLambdaProcessorProperty
,None
]) – The InputLambdaProcessor that is used to preprocess the records in the stream before being processed by your application code.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics input_processing_configuration_property = kinesisanalytics.CfnApplicationV2.InputProcessingConfigurationProperty( input_lambda_processor=kinesisanalytics.CfnApplicationV2.InputLambdaProcessorProperty( resource_arn="resourceArn" ) )
Attributes
-
input_lambda_processor
¶ //docs.aws.amazon.com/kinesisanalytics/latest/apiv2/API_InputLambdaProcessor.html>`_ that is used to preprocess the records in the stream before being processed by your application code.
- Link
- Type
The `InputLambdaProcessor <https
- Return type
Union
[IResolvable
,InputLambdaProcessorProperty
,None
]
InputProperty¶
-
class
CfnApplicationV2.
InputProperty
(*, input_schema, name_prefix, input_parallelism=None, input_processing_configuration=None, kinesis_firehose_input=None, kinesis_streams_input=None)¶ Bases:
object
When you configure the application input for a SQL-based Kinesis Data Analytics application, you specify the streaming source, the in-application stream name that is created, and the mapping between the two.
- Parameters
input_schema (
Union
[IResolvable
,InputSchemaProperty
]) – Describes the format of the data in the streaming source, and how each data element maps to corresponding columns in the in-application stream that is being created. Also used to describe the format of the reference data source.name_prefix (
str
) – The name prefix to use when creating an in-application stream. Suppose that you specify a prefix ”MyInApplicationStream
.” Kinesis Data Analytics then creates one or more (as per theInputParallelism
count you specified) in-application streams with the names ”MyInApplicationStream_001
,” ”MyInApplicationStream_002
,” and so on.input_parallelism (
Union
[IResolvable
,InputParallelismProperty
,None
]) – Describes the number of in-application streams to create.input_processing_configuration (
Union
[IResolvable
,InputProcessingConfigurationProperty
,None
]) –The InputProcessingConfiguration for the input. An input processor transforms records as they are received from the stream, before the application’s SQL code executes. Currently, the only input processing configuration available is InputLambdaProcessor .
kinesis_firehose_input (
Union
[IResolvable
,KinesisFirehoseInputProperty
,None
]) – If the streaming source is an Amazon Kinesis Data Firehose delivery stream, identifies the delivery stream’s ARN.kinesis_streams_input (
Union
[IResolvable
,KinesisStreamsInputProperty
,None
]) – If the streaming source is an Amazon Kinesis data stream, identifies the stream’s Amazon Resource Name (ARN).
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics input_property = kinesisanalytics.CfnApplicationV2.InputProperty( input_schema=kinesisanalytics.CfnApplicationV2.InputSchemaProperty( record_columns=[kinesisanalytics.CfnApplicationV2.RecordColumnProperty( name="name", sql_type="sqlType", # the properties below are optional mapping="mapping" )], record_format=kinesisanalytics.CfnApplicationV2.RecordFormatProperty( record_format_type="recordFormatType", # the properties below are optional mapping_parameters=kinesisanalytics.CfnApplicationV2.MappingParametersProperty( csv_mapping_parameters=kinesisanalytics.CfnApplicationV2.CSVMappingParametersProperty( record_column_delimiter="recordColumnDelimiter", record_row_delimiter="recordRowDelimiter" ), json_mapping_parameters=kinesisanalytics.CfnApplicationV2.JSONMappingParametersProperty( record_row_path="recordRowPath" ) ) ), # the properties below are optional record_encoding="recordEncoding" ), name_prefix="namePrefix", # the properties below are optional input_parallelism=kinesisanalytics.CfnApplicationV2.InputParallelismProperty( count=123 ), input_processing_configuration=kinesisanalytics.CfnApplicationV2.InputProcessingConfigurationProperty( input_lambda_processor=kinesisanalytics.CfnApplicationV2.InputLambdaProcessorProperty( resource_arn="resourceArn" ) ), kinesis_firehose_input=kinesisanalytics.CfnApplicationV2.KinesisFirehoseInputProperty( resource_arn="resourceArn" ), kinesis_streams_input=kinesisanalytics.CfnApplicationV2.KinesisStreamsInputProperty( resource_arn="resourceArn" ) )
Attributes
-
input_parallelism
¶ Describes the number of in-application streams to create.
-
input_processing_configuration
¶ //docs.aws.amazon.com/kinesisanalytics/latest/apiv2/API_InputLambdaProcessor.html>`_ .
- Link
- Type
The `InputProcessingConfiguration <https
- Type
//docs.aws.amazon.com/kinesisanalytics/latest/apiv2/API_InputProcessingConfiguration.html>`_ for the input. An input processor transforms records as they are received from the stream, before the application’s SQL code executes. Currently, the only input processing configuration available is `InputLambdaProcessor <https
- Return type
Union
[IResolvable
,InputProcessingConfigurationProperty
,None
]
-
input_schema
¶ Describes the format of the data in the streaming source, and how each data element maps to corresponding columns in the in-application stream that is being created.
Also used to describe the format of the reference data source.
-
kinesis_firehose_input
¶ If the streaming source is an Amazon Kinesis Data Firehose delivery stream, identifies the delivery stream’s ARN.
-
kinesis_streams_input
¶ If the streaming source is an Amazon Kinesis data stream, identifies the stream’s Amazon Resource Name (ARN).
-
name_prefix
¶ The name prefix to use when creating an in-application stream.
Suppose that you specify a prefix ”
MyInApplicationStream
.” Kinesis Data Analytics then creates one or more (as per theInputParallelism
count you specified) in-application streams with the names ”MyInApplicationStream_001
,” ”MyInApplicationStream_002
,” and so on.
InputSchemaProperty¶
-
class
CfnApplicationV2.
InputSchemaProperty
(*, record_columns, record_format, record_encoding=None)¶ Bases:
object
For a SQL-based Kinesis Data Analytics application, describes the format of the data in the streaming source, and how each data element maps to corresponding columns created in the in-application stream.
- Parameters
record_columns (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,RecordColumnProperty
]]]) – A list ofRecordColumn
objects.record_format (
Union
[IResolvable
,RecordFormatProperty
]) – Specifies the format of the records on the streaming source.record_encoding (
Optional
[str
]) – Specifies the encoding of the records in the streaming source. For example, UTF-8.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics input_schema_property = kinesisanalytics.CfnApplicationV2.InputSchemaProperty( record_columns=[kinesisanalytics.CfnApplicationV2.RecordColumnProperty( name="name", sql_type="sqlType", # the properties below are optional mapping="mapping" )], record_format=kinesisanalytics.CfnApplicationV2.RecordFormatProperty( record_format_type="recordFormatType", # the properties below are optional mapping_parameters=kinesisanalytics.CfnApplicationV2.MappingParametersProperty( csv_mapping_parameters=kinesisanalytics.CfnApplicationV2.CSVMappingParametersProperty( record_column_delimiter="recordColumnDelimiter", record_row_delimiter="recordRowDelimiter" ), json_mapping_parameters=kinesisanalytics.CfnApplicationV2.JSONMappingParametersProperty( record_row_path="recordRowPath" ) ) ), # the properties below are optional record_encoding="recordEncoding" )
Attributes
-
record_columns
¶ A list of
RecordColumn
objects.
-
record_encoding
¶ Specifies the encoding of the records in the streaming source.
For example, UTF-8.
-
record_format
¶ Specifies the format of the records on the streaming source.
JSONMappingParametersProperty¶
-
class
CfnApplicationV2.
JSONMappingParametersProperty
(*, record_row_path)¶ Bases:
object
For a SQL-based Kinesis Data Analytics application, provides additional mapping information when JSON is the record format on the streaming source.
- Parameters
record_row_path (
str
) – The path to the top-level parent that contains the records.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics j_sONMapping_parameters_property = kinesisanalytics.CfnApplicationV2.JSONMappingParametersProperty( record_row_path="recordRowPath" )
Attributes
-
record_row_path
¶ The path to the top-level parent that contains the records.
KinesisFirehoseInputProperty¶
-
class
CfnApplicationV2.
KinesisFirehoseInputProperty
(*, resource_arn)¶ Bases:
object
For a SQL-based Kinesis Data Analytics application, identifies a Kinesis Data Firehose delivery stream as the streaming source.
You provide the delivery stream’s Amazon Resource Name (ARN).
- Parameters
resource_arn (
str
) – The Amazon Resource Name (ARN) of the delivery stream.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics kinesis_firehose_input_property = kinesisanalytics.CfnApplicationV2.KinesisFirehoseInputProperty( resource_arn="resourceArn" )
Attributes
-
resource_arn
¶ The Amazon Resource Name (ARN) of the delivery stream.
KinesisStreamsInputProperty¶
-
class
CfnApplicationV2.
KinesisStreamsInputProperty
(*, resource_arn)¶ Bases:
object
Identifies a Kinesis data stream as the streaming source.
You provide the stream’s Amazon Resource Name (ARN).
- Parameters
resource_arn (
str
) – The ARN of the input Kinesis data stream to read.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics kinesis_streams_input_property = kinesisanalytics.CfnApplicationV2.KinesisStreamsInputProperty( resource_arn="resourceArn" )
Attributes
-
resource_arn
¶ The ARN of the input Kinesis data stream to read.
MappingParametersProperty¶
-
class
CfnApplicationV2.
MappingParametersProperty
(*, csv_mapping_parameters=None, json_mapping_parameters=None)¶ Bases:
object
When you configure a SQL-based Kinesis Data Analytics application’s input at the time of creating or updating an application, provides additional mapping information specific to the record format (such as JSON, CSV, or record fields delimited by some delimiter) on the streaming source.
- Parameters
csv_mapping_parameters (
Union
[IResolvable
,CSVMappingParametersProperty
,None
]) – Provides additional mapping information when the record format uses delimiters (for example, CSV).json_mapping_parameters (
Union
[IResolvable
,JSONMappingParametersProperty
,None
]) – Provides additional mapping information when JSON is the record format on the streaming source.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics mapping_parameters_property = kinesisanalytics.CfnApplicationV2.MappingParametersProperty( csv_mapping_parameters=kinesisanalytics.CfnApplicationV2.CSVMappingParametersProperty( record_column_delimiter="recordColumnDelimiter", record_row_delimiter="recordRowDelimiter" ), json_mapping_parameters=kinesisanalytics.CfnApplicationV2.JSONMappingParametersProperty( record_row_path="recordRowPath" ) )
Attributes
-
csv_mapping_parameters
¶ Provides additional mapping information when the record format uses delimiters (for example, CSV).
-
json_mapping_parameters
¶ Provides additional mapping information when JSON is the record format on the streaming source.
MavenReferenceProperty¶
-
class
CfnApplicationV2.
MavenReferenceProperty
(*, artifact_id, group_id, version)¶ Bases:
object
The information required to specify a Maven reference.
You can use Maven references to specify dependency JAR files.
- Parameters
artifact_id (
str
) – The artifact ID of the Maven reference.group_id (
str
) – The group ID of the Maven reference.version (
str
) – The version of the Maven reference.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics maven_reference_property = kinesisanalytics.CfnApplicationV2.MavenReferenceProperty( artifact_id="artifactId", group_id="groupId", version="version" )
Attributes
-
artifact_id
¶ The artifact ID of the Maven reference.
-
group_id
¶ The group ID of the Maven reference.
-
version
¶ The version of the Maven reference.
MonitoringConfigurationProperty¶
-
class
CfnApplicationV2.
MonitoringConfigurationProperty
(*, configuration_type, log_level=None, metrics_level=None)¶ Bases:
object
Describes configuration parameters for Amazon CloudWatch logging for a Java-based Kinesis Data Analytics application.
For more information about CloudWatch logging, see Monitoring .
- Parameters
configuration_type (
str
) – Describes whether to use the default CloudWatch logging configuration for an application. You must set this property toCUSTOM
in order to set theLogLevel
orMetricsLevel
parameters.log_level (
Optional
[str
]) – Describes the verbosity of the CloudWatch Logs for an application.metrics_level (
Optional
[str
]) – Describes the granularity of the CloudWatch Logs for an application. TheParallelism
level is not recommended for applications with a Parallelism over 64 due to excessive costs.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics monitoring_configuration_property = kinesisanalytics.CfnApplicationV2.MonitoringConfigurationProperty( configuration_type="configurationType", # the properties below are optional log_level="logLevel", metrics_level="metricsLevel" )
Attributes
-
configuration_type
¶ Describes whether to use the default CloudWatch logging configuration for an application.
You must set this property to
CUSTOM
in order to set theLogLevel
orMetricsLevel
parameters.
-
log_level
¶ Describes the verbosity of the CloudWatch Logs for an application.
-
metrics_level
¶ Describes the granularity of the CloudWatch Logs for an application.
The
Parallelism
level is not recommended for applications with a Parallelism over 64 due to excessive costs.
ParallelismConfigurationProperty¶
-
class
CfnApplicationV2.
ParallelismConfigurationProperty
(*, configuration_type, auto_scaling_enabled=None, parallelism=None, parallelism_per_kpu=None)¶ Bases:
object
Describes parameters for how a Flink-based Kinesis Data Analytics application executes multiple tasks simultaneously.
For more information about parallelism, see Parallel Execution in the Apache Flink Documentation .
- Parameters
configuration_type (
str
) – Describes whether the application uses the default parallelism for the Kinesis Data Analytics service. You must set this property toCUSTOM
in order to change your application’sAutoScalingEnabled
,Parallelism
, orParallelismPerKPU
properties.auto_scaling_enabled (
Union
[bool
,IResolvable
,None
]) – Describes whether the Kinesis Data Analytics service can increase the parallelism of the application in response to increased throughput.parallelism (
Union
[int
,float
,None
]) – Describes the initial number of parallel tasks that a Java-based Kinesis Data Analytics application can perform. The Kinesis Data Analytics service can increase this number automatically if ParallelismConfiguration:AutoScalingEnabled is set totrue
.parallelism_per_kpu (
Union
[int
,float
,None
]) – Describes the number of parallel tasks that a Java-based Kinesis Data Analytics application can perform per Kinesis Processing Unit (KPU) used by the application. For more information about KPUs, see Amazon Kinesis Data Analytics Pricing .
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics parallelism_configuration_property = kinesisanalytics.CfnApplicationV2.ParallelismConfigurationProperty( configuration_type="configurationType", # the properties below are optional auto_scaling_enabled=False, parallelism=123, parallelism_per_kpu=123 )
Attributes
-
auto_scaling_enabled
¶ Describes whether the Kinesis Data Analytics service can increase the parallelism of the application in response to increased throughput.
-
configuration_type
¶ Describes whether the application uses the default parallelism for the Kinesis Data Analytics service.
You must set this property to
CUSTOM
in order to change your application’sAutoScalingEnabled
,Parallelism
, orParallelismPerKPU
properties.
-
parallelism
¶ Describes the initial number of parallel tasks that a Java-based Kinesis Data Analytics application can perform.
The Kinesis Data Analytics service can increase this number automatically if ParallelismConfiguration:AutoScalingEnabled is set to
true
.
-
parallelism_per_kpu
¶ Describes the number of parallel tasks that a Java-based Kinesis Data Analytics application can perform per Kinesis Processing Unit (KPU) used by the application.
For more information about KPUs, see Amazon Kinesis Data Analytics Pricing .
PropertyGroupProperty¶
-
class
CfnApplicationV2.
PropertyGroupProperty
(*, property_group_id=None, property_map=None)¶ Bases:
object
Property key-value pairs passed into an application.
- Parameters
property_group_id (
Optional
[str
]) – Describes the key of an application execution property key-value pair.property_map (
Optional
[Any
]) – Describes the value of an application execution property key-value pair.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics # property_map: Any property_group_property = kinesisanalytics.CfnApplicationV2.PropertyGroupProperty( property_group_id="propertyGroupId", property_map=property_map )
Attributes
-
property_group_id
¶ Describes the key of an application execution property key-value pair.
-
property_map
¶ Describes the value of an application execution property key-value pair.
RecordColumnProperty¶
-
class
CfnApplicationV2.
RecordColumnProperty
(*, name, sql_type, mapping=None)¶ Bases:
object
For a SQL-based Kinesis Data Analytics application, describes the mapping of each data element in the streaming source to the corresponding column in the in-application stream.
Also used to describe the format of the reference data source.
- Parameters
name (
str
) – The name of the column that is created in the in-application input stream or reference table.sql_type (
str
) – The type of column created in the in-application input stream or reference table.mapping (
Optional
[str
]) – A reference to the data element in the streaming input or the reference data source.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics record_column_property = kinesisanalytics.CfnApplicationV2.RecordColumnProperty( name="name", sql_type="sqlType", # the properties below are optional mapping="mapping" )
Attributes
-
mapping
¶ A reference to the data element in the streaming input or the reference data source.
-
name
¶ The name of the column that is created in the in-application input stream or reference table.
-
sql_type
¶ The type of column created in the in-application input stream or reference table.
RecordFormatProperty¶
-
class
CfnApplicationV2.
RecordFormatProperty
(*, record_format_type, mapping_parameters=None)¶ Bases:
object
For a SQL-based Kinesis Data Analytics application, describes the record format and relevant mapping information that should be applied to schematize the records on the stream.
- Parameters
record_format_type (
str
) – The type of record format.mapping_parameters (
Union
[IResolvable
,MappingParametersProperty
,None
]) – When you configure application input at the time of creating or updating an application, provides additional mapping information specific to the record format (such as JSON, CSV, or record fields delimited by some delimiter) on the streaming source.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics record_format_property = kinesisanalytics.CfnApplicationV2.RecordFormatProperty( record_format_type="recordFormatType", # the properties below are optional mapping_parameters=kinesisanalytics.CfnApplicationV2.MappingParametersProperty( csv_mapping_parameters=kinesisanalytics.CfnApplicationV2.CSVMappingParametersProperty( record_column_delimiter="recordColumnDelimiter", record_row_delimiter="recordRowDelimiter" ), json_mapping_parameters=kinesisanalytics.CfnApplicationV2.JSONMappingParametersProperty( record_row_path="recordRowPath" ) ) )
Attributes
-
mapping_parameters
¶ When you configure application input at the time of creating or updating an application, provides additional mapping information specific to the record format (such as JSON, CSV, or record fields delimited by some delimiter) on the streaming source.
-
record_format_type
¶ The type of record format.
S3ContentBaseLocationProperty¶
-
class
CfnApplicationV2.
S3ContentBaseLocationProperty
(*, base_path, bucket_arn)¶ Bases:
object
The base location of the Amazon Data Analytics application.
- Parameters
base_path (
str
) – The base path for the S3 bucket.bucket_arn (
str
) – The Amazon Resource Name (ARN) of the S3 bucket.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics s3_content_base_location_property = kinesisanalytics.CfnApplicationV2.S3ContentBaseLocationProperty( base_path="basePath", bucket_arn="bucketArn" )
Attributes
-
base_path
¶ The base path for the S3 bucket.
-
bucket_arn
¶ The Amazon Resource Name (ARN) of the S3 bucket.
S3ContentLocationProperty¶
-
class
CfnApplicationV2.
S3ContentLocationProperty
(*, bucket_arn=None, file_key=None, object_version=None)¶ Bases:
object
The location of an application or a custom artifact.
- Parameters
bucket_arn (
Optional
[str
]) – The Amazon Resource Name (ARN) for the S3 bucket containing the application code.file_key (
Optional
[str
]) – The file key for the object containing the application code.object_version (
Optional
[str
]) – The version of the object containing the application code.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics s3_content_location_property = kinesisanalytics.CfnApplicationV2.S3ContentLocationProperty( bucket_arn="bucketArn", file_key="fileKey", object_version="objectVersion" )
Attributes
-
bucket_arn
¶ The Amazon Resource Name (ARN) for the S3 bucket containing the application code.
-
file_key
¶ The file key for the object containing the application code.
-
object_version
¶ The version of the object containing the application code.
SqlApplicationConfigurationProperty¶
-
class
CfnApplicationV2.
SqlApplicationConfigurationProperty
(*, inputs=None)¶ Bases:
object
Describes the inputs, outputs, and reference data sources for a SQL-based Kinesis Data Analytics application.
- Parameters
inputs (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,InputProperty
]],None
]) – The array of Input objects describing the input streams used by the application.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics sql_application_configuration_property = kinesisanalytics.CfnApplicationV2.SqlApplicationConfigurationProperty( inputs=[kinesisanalytics.CfnApplicationV2.InputProperty( input_schema=kinesisanalytics.CfnApplicationV2.InputSchemaProperty( record_columns=[kinesisanalytics.CfnApplicationV2.RecordColumnProperty( name="name", sql_type="sqlType", # the properties below are optional mapping="mapping" )], record_format=kinesisanalytics.CfnApplicationV2.RecordFormatProperty( record_format_type="recordFormatType", # the properties below are optional mapping_parameters=kinesisanalytics.CfnApplicationV2.MappingParametersProperty( csv_mapping_parameters=kinesisanalytics.CfnApplicationV2.CSVMappingParametersProperty( record_column_delimiter="recordColumnDelimiter", record_row_delimiter="recordRowDelimiter" ), json_mapping_parameters=kinesisanalytics.CfnApplicationV2.JSONMappingParametersProperty( record_row_path="recordRowPath" ) ) ), # the properties below are optional record_encoding="recordEncoding" ), name_prefix="namePrefix", # the properties below are optional input_parallelism=kinesisanalytics.CfnApplicationV2.InputParallelismProperty( count=123 ), input_processing_configuration=kinesisanalytics.CfnApplicationV2.InputProcessingConfigurationProperty( input_lambda_processor=kinesisanalytics.CfnApplicationV2.InputLambdaProcessorProperty( resource_arn="resourceArn" ) ), kinesis_firehose_input=kinesisanalytics.CfnApplicationV2.KinesisFirehoseInputProperty( resource_arn="resourceArn" ), kinesis_streams_input=kinesisanalytics.CfnApplicationV2.KinesisStreamsInputProperty( resource_arn="resourceArn" ) )] )
Attributes
-
inputs
¶ //docs.aws.amazon.com/kinesisanalytics/latest/apiv2/API_Input.html>`_ objects describing the input streams used by the application.
- Link
- Type
The array of `Input <https
- Return type
Union
[IResolvable
,List
[Union
[IResolvable
,InputProperty
]],None
]
ZeppelinApplicationConfigurationProperty¶
-
class
CfnApplicationV2.
ZeppelinApplicationConfigurationProperty
(*, catalog_configuration=None, custom_artifacts_configuration=None, deploy_as_application_configuration=None, monitoring_configuration=None)¶ Bases:
object
The configuration of a Kinesis Data Analytics Studio notebook.
- Parameters
catalog_configuration (
Union
[IResolvable
,CatalogConfigurationProperty
,None
]) – The Amazon Glue Data Catalog that you use in queries in a Kinesis Data Analytics Studio notebook.custom_artifacts_configuration (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,CustomArtifactConfigurationProperty
]],None
]) – A list ofCustomArtifactConfiguration
objects.deploy_as_application_configuration (
Union
[IResolvable
,DeployAsApplicationConfigurationProperty
,None
]) – The information required to deploy a Kinesis Data Analytics Studio notebook as an application with durable state.monitoring_configuration (
Union
[IResolvable
,ZeppelinMonitoringConfigurationProperty
,None
]) – The monitoring configuration of a Kinesis Data Analytics Studio notebook.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics zeppelin_application_configuration_property = kinesisanalytics.CfnApplicationV2.ZeppelinApplicationConfigurationProperty( catalog_configuration=kinesisanalytics.CfnApplicationV2.CatalogConfigurationProperty( glue_data_catalog_configuration=kinesisanalytics.CfnApplicationV2.GlueDataCatalogConfigurationProperty( database_arn="databaseArn" ) ), custom_artifacts_configuration=[kinesisanalytics.CfnApplicationV2.CustomArtifactConfigurationProperty( artifact_type="artifactType", # the properties below are optional maven_reference=kinesisanalytics.CfnApplicationV2.MavenReferenceProperty( artifact_id="artifactId", group_id="groupId", version="version" ), s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentLocationProperty( bucket_arn="bucketArn", file_key="fileKey", object_version="objectVersion" ) )], deploy_as_application_configuration=kinesisanalytics.CfnApplicationV2.DeployAsApplicationConfigurationProperty( s3_content_location=kinesisanalytics.CfnApplicationV2.S3ContentBaseLocationProperty( base_path="basePath", bucket_arn="bucketArn" ) ), monitoring_configuration=kinesisanalytics.CfnApplicationV2.ZeppelinMonitoringConfigurationProperty( log_level="logLevel" ) )
Attributes
-
catalog_configuration
¶ The Amazon Glue Data Catalog that you use in queries in a Kinesis Data Analytics Studio notebook.
- Link
- Return type
Union
[IResolvable
,CatalogConfigurationProperty
,None
]
-
custom_artifacts_configuration
¶ A list of
CustomArtifactConfiguration
objects.- Link
- Return type
Union
[IResolvable
,List
[Union
[IResolvable
,CustomArtifactConfigurationProperty
]],None
]
-
deploy_as_application_configuration
¶ The information required to deploy a Kinesis Data Analytics Studio notebook as an application with durable state.
- Link
- Return type
Union
[IResolvable
,DeployAsApplicationConfigurationProperty
,None
]
-
monitoring_configuration
¶ The monitoring configuration of a Kinesis Data Analytics Studio notebook.
- Link
- Return type
Union
[IResolvable
,ZeppelinMonitoringConfigurationProperty
,None
]
ZeppelinMonitoringConfigurationProperty¶
-
class
CfnApplicationV2.
ZeppelinMonitoringConfigurationProperty
(*, log_level=None)¶ Bases:
object
Describes configuration parameters for Amazon CloudWatch logging for a Kinesis Data Analytics Studio notebook.
For more information about CloudWatch logging, see Monitoring .
- Parameters
log_level (
Optional
[str
]) – The verbosity of the CloudWatch Logs for an application. You can set it toINFO
,WARN
,ERROR
, orDEBUG
.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_kinesisanalytics as kinesisanalytics zeppelin_monitoring_configuration_property = kinesisanalytics.CfnApplicationV2.ZeppelinMonitoringConfigurationProperty( log_level="logLevel" )
Attributes
-
log_level
¶ The verbosity of the CloudWatch Logs for an application.
You can set it to
INFO
,WARN
,ERROR
, orDEBUG
.